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Specification tests for non-Gaussian maximum likelihood estimators

Gabriele Fiorentini () and Enrique Sentana ()

No 12934, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for GARCH models and in many empirically relevant macro and finance applications involving VARs and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.

Keywords: Durbin-Wu-Hausman Tests; Partial Adaptivity; Semiparametric Estimators; Singular Covariance Matrices (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 C32 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
Date: 2018-05
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Related works:
Working Paper: Specification Tests for Non-Gaussian Maximum Likelihood Estimators (2018) Downloads
Working Paper: Specification tests for non-Gaussian maximum likelihood estimators (2018) Downloads
Working Paper: Specification tests for non-Gaussian maximum likelihood estimators (2018) Downloads
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